Botnet Attacks in Computer Network Security

Abstract: This paper introduces a deep learning-oriented framework for identifying and preventing botnet-caused DDoS attacks in Software-Defined Networking (SDN). Conventional security techniques experience high false alarms and slow detection because of the dynamic nature of botnet attacks. To improve this, the system utilizes Convolutional Neural Networks (CNNs) to detect anomalies in real-time and a graph theory-oriented dynamic flow management algorithm for preventing attacks. Experimental assessments with CICIDS 2017 and Bot-IoT datasets and a simulated SDN testbed (Mininet) indicate that the system detects attacks with 98.2% accuracy, sustains 85% network throughput during attack, and neutralizes threats in five seconds. In comparison to traditional models such as KNN, SVM, and Random Forest, the CNN-based model exhibits better accuracy, flexibility, and scalability. This work adds to SDN security by combining real-time traffic observation, deep learning, and adaptive flow control for better network cyber-threat resilience. Potential future developments are reinforcement learning-based defenses, enlarging datasets, and empirical SDN evaluations